Intelligent food material recognition, weighing and child nutrition assessment system

Abstract

The present application relates to the technical field of children nutrition assessment, and provides a kind of intelligent food material identification weighing and children nutrition assessment system, comprising: weighing module, for accurately weighing food material weight;Image recognition module, integrated multispectral camera and edge computing unit, through convolutional neural network model, multi-level feature extraction is carried out to food material image, and food material category and subdivision category are identified;Database module, built-in latest version Chinese food composition table, stores the nutrient component data of each kind of food material per 100 grams;Calculation module, according to the weight data output by weighing module and the food material category identified by image recognition module. Through the linkage of image recognition and weighing module, the synchronous acquisition of food material category and weight is realized, non-professional personnel can operate independently, the evaluation efficiency is improved by more than 70%, long-term data accumulation and trend analysis are supported, help parents to master children's nutrition status comprehensively, avoid phase nutrition imbalance.

Description

Technical Field

[0001] This invention relates to the field of children's nutritional assessment technology, specifically to an intelligent food identification and weighing system for children's nutritional assessment. Background Technology

[0002] Nutritional assessment for children is a crucial step in ensuring their healthy growth. Traditional methods rely on nutritionists manually weighing ingredients and consulting paper or electronic food composition tables for calculations, a cumbersome and time-consuming process. With the development of smart technology, some systems are attempting to perform nutritional calculations by combining electronic scales with simple databases.

[0003] However, some devices still only support manual input of food types after weighing, relying on user operation and unable to automatically identify food types. Nutritional data is outdated and cannot be adapted to the latest dietary standards. They also lack personalized recommendations for children's age groups, and the assessment results are out of touch with actual needs. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides an intelligent food identification and weighing system for children's nutritional assessment. This system solves the problems that some devices only support manual input of food types after weighing, rely on user operation, cannot automatically identify food types, have outdated nutritional data, cannot adapt to the latest dietary standards, lack personalized recommendations for children's age groups, and have assessment results that are out of touch with actual needs.

[0005] To achieve the above objectives, the present invention provides the following technical solution: an intelligent food identification, weighing, and children's nutritional assessment system, comprising:

[0006] The weighing module is used to accurately weigh the ingredients.

[0007] The image recognition module integrates a multispectral camera and an edge computing unit. It uses a convolutional neural network model to extract multi-level features from food images and identify the types and subcategories of food.

[0008] The database module has a built-in latest version of the Chinese Food Composition Table, which stores the nutritional composition data of various ingredients per 100 grams.

[0009] The calculation module, based on the weight data output by the weighing module and the food type identified by the image recognition module, extracts the corresponding nutritional data from the database module and calculates the actual intake according to the formula:

[0010] Actual intake = weighed weight / 100 × nutrient value in database;

[0011] The assessment module compares the total nutrient data output by the calculation module with the preset recommended intake for children's age groups, and generates a nutrition assessment report and dietary recommendations.

[0012] Preferably, the image recognition module adopts a convolutional neural network model, which supports the classification and recognition of multiple categories of food ingredients.

[0013] Preferably, the weighing module and the image recognition module synchronize data via Bluetooth 5.0 protocol. The camera is automatically triggered to capture images during weighing, and the weight and recognition results are superimposed and displayed on the touch screen. Users can manually adjust the food classification or weight data.

[0014] Preferably, the database module has a built-in food equivalent substitution algorithm. When the inventory of a certain food ingredient is insufficient, it recommends alternative food ingredients based on the similarity of nutritional components and dynamically adjusts the intake calculation. The formula is as follows:

[0015] The weight of the substitute ingredient = the target nutrient value of the original ingredient / the nutrient value per 100 grams of the substitute ingredient × 100.

[0016] Preferably, the assessment module integrates a children's health data interface, supporting manual input or synchronization of height, weight, and activity data through smart wearable devices, and dynamically adjusting the recommended intake based on the Harris-Benedict formula: Daily energy requirement = Basal metabolic rate × Activity coefficient + Growth compensation value, wherein the growth compensation value is set according to age and growth curve.

[0017] Preferably, the system supports a multi-user management mode, distinguishes user accounts through fingerprint or facial recognition, independently stores the historical nutritional data of each child, and generates personalized trend analysis reports, including nutrient intake line graphs, target achievement rate radar charts, and percentiles compared with children of the same age.

[0018] Preferably, the computing module incorporates blockchain technology to encrypt and store daily intake data in a distributed ledger, ensuring that the data is tamper-proof and supporting authorized access to historical records by medical institutions.

[0019] Preferably, the system has a built-in voice interaction unit that supports voice command control of weighing, querying of nutritional components and broadcasting of assessment results, and uses natural language processing technology to analyze user questions, such as "Is today's protein intake up to standard?"

[0020] Preferably, the assessment module connects to the API of a third-party nutritional supplement e-commerce platform. When it detects that the intake of a specific nutrient is insufficient for three consecutive days, the system will provide corresponding dietary improvement suggestions or recipes, and can also automatically generate purchase links to recommend nutritional supplements.

[0021] Preferably, the system is equipped with an ultraviolet disinfection unit integrated under the weighing tray, which automatically starts a 30-second sterilization cycle after each use to ensure the hygiene and safety of the food contact surface.

[0022] This system achieves intelligent assessment of children's nutrition through multi-module collaboration. The specific process is as follows:

[0023] Step 1: The user places the ingredients on the weighing module's tray. A high-precision pressure sensor collects weight data in real time (accuracy ±0.1g), and a temperature compensation circuit eliminates environmental interference. After weighing is triggered, the multispectral camera of the image recognition module automatically captures images of the ingredients. The edge computing unit calls a pre-trained convolutional neural network model to extract color, texture, and shape features, compares them with 500 ingredient features in the local database, and outputs the ingredient type and sub-category. If automatic recognition fails, the user can manually select ingredients or correct the classification via the touch screen.

[0024] Step 2: The weighing and ingredient classification data are transmitted to the calculation module via Bluetooth 5.0. The module retrieves the nutritional information for each 100 grams of the corresponding ingredient from the database module and calculates the actual intake based on the following formula:

[0025] Actual nutrients = (Weighing weight / 100 × Nutrient value) × Cooking loss factor

[0026] The cooking loss coefficient is dynamically matched according to the food processing method; data from multiple meals is automatically accumulated, and the calculation module summarizes the total intake of energy, carbohydrates, protein, fat, trace elements, macro elements, vitamins, dietary fiber, water, etc. on a daily basis.

[0027] Step 3: The assessment module compares the total intake with the recommended values ​​for children aged 0-18 years in the database and generates a nutritional deficit report using a deviation analysis algorithm. If insufficient calcium intake is detected for three consecutive days, the system pushes a calcium supplement recipe to the parents' mobile phones via the wireless communication module and links with third-party e-commerce platforms to recommend children's calcium tablets. All data is stored after being encrypted with blockchain, and medical institutions can access historical records for analysis through authorized interfaces.

[0028] This invention provides an intelligent food identification and weighing system for children's nutritional assessment. It offers the following advantages:

[0029] 1. This invention achieves simultaneous acquisition of food type and weight through the linkage of image recognition and weighing modules. Non-professionals can operate it independently, improving assessment efficiency by more than 70%. It supports long-term data accumulation and trend analysis, helping parents to fully understand their children's nutritional status and avoid periodic nutritional imbalances.

[0030] 2. This invention combines high-precision weighing with a real-time updated database, reducing the nutrient calculation error rate to ≤3%. Based on dynamic comparison of recommended intakes for children of different age groups, the system automatically generates targeted dietary guidance, increasing the adoption rate by 50%. Attached Figure Description

[0031] Figure 1 This is a system diagram of the present invention;

[0032] Figure 2 This is a schematic diagram of the system structure of the evaluation module in this invention;

[0033] Figure 3 This is a schematic diagram of the system structure of the database module in this invention;

[0034] Figure 4 This is a schematic diagram of the system structure of the image recognition module in this invention. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] Please see the appendix Figure 1 This invention provides an intelligent food identification and weighing system and a children's nutritional assessment system, comprising:

[0037] The weighing module is used to accurately weigh food ingredients. It synchronizes data with the image recognition module via Bluetooth 5.0 protocol. The camera is automatically triggered to take pictures during weighing, and the weight and recognition results are superimposed on the touch screen. Users can manually adjust the food category or weight data.

[0038] Please see the appendix Figure 4 The image recognition module, which adopts a convolutional neural network model, supports the classification and recognition of multiple types of food. It integrates a multispectral camera and an edge computing unit, and uses the convolutional neural network model to extract multi-level features from food images to identify food types and subcategories.

[0039] Please see the appendix Figure 3 The database module includes the latest version of the Chinese Food Composition Table, storing nutritional information for 100 grams of various ingredients, as well as an equivalent substitution algorithm. When an ingredient is in short supply, it recommends alternative ingredients based on nutritional similarity and dynamically adjusts the intake calculation. The formula is as follows:

[0040] The weight of the substitute ingredient = the target nutrient value of the original ingredient / the nutrient value per 100 grams of the substitute ingredient × 100;

[0041] The calculation module, based on the weight data output by the weighing module and the food type identified by the image recognition module, extracts the corresponding nutritional data from the database module and calculates the actual intake according to the formula:

[0042] Actual intake = weighed weight / 100 × nutrient value in database;

[0043] By introducing blockchain technology, daily intake data is encrypted and stored in a distributed ledger to ensure that the data is immutable and to support authorized access to historical records by medical institutions.

[0044] Please see the appendix Figure 2 The assessment module compares the total nutrient data output by the calculation module with the preset recommended intake for children's age group, generating a nutrition assessment report and dietary recommendations. It integrates a children's health data interface, supporting manual input or synchronization of height, weight, and activity data through smart wearable devices. It dynamically adjusts the recommended intake based on the Harris-Benedict formula: Daily energy requirement = Basal metabolic rate × Activity coefficient + Growth compensation value, where the growth compensation value is set according to age and growth curve. It connects to the API of third-party nutritional supplement e-commerce platforms. When it detects that the intake of a specific nutrient is insufficient for three consecutive days, the system will provide corresponding dietary improvement suggestions or recipes, and can automatically generate purchase links to recommend nutritional supplements.

[0045] The multi-user management mode distinguishes user accounts through fingerprint or facial recognition, independently stores each child's historical nutritional data, and generates personalized trend analysis reports, including nutrient intake line graphs, target achievement rate radar charts, and percentile comparisons with peers. It has a built-in voice interaction unit that supports voice command control of weighing, querying nutritional components, and broadcasting assessment results. It also uses natural language processing technology to analyze user questions, such as "Is today's protein intake up to standard?" It is equipped with an ultraviolet disinfection unit integrated under the weighing tray, which automatically starts a 30-second sterilization cycle after each use to ensure the hygiene and safety of food contact surfaces.

[0046] The following description, in conjunction with specific embodiments, will be provided.

[0047] Example 1

[0048] This system was used to evaluate the preparation of "tomato beef spaghetti" for lunch to a 5-year-old child.

[0049] The raw beef (80g), tomato (50g), and pasta (60g) were weighed separately; the image recognition module successfully identified the ingredients and classified them as "beef hind leg meat", "tomato", and "durum wheat pasta"; the parents selected the cooking methods as "boiled pasta" and "stir-fried beef".

[0050] Nutritional calculation:

[0051] Data retrieved from the database: Beef hind leg (protein 20g / 100g, iron 2.5mg / 100g), tomato (vitamin C 14mg / 100g), pasta (carbohydrates 75g / 100g).

[0052] The calculation module, combining cooking loss coefficients (10% protein loss in stir-fried beef, 30% vitamin C loss in boiled tomatoes), concludes that:

[0053] Beef protein: 80 / 100 × 20 × 0.9 = 14.4g;

[0054] Vitamin C content of tomatoes: 50 / 100 × 14 × 0.7 = 4.9 mg.

[0055] The system compared the recommended daily intake for 5-year-old children (20g protein, 40mg vitamin C) and indicated that "protein intake is adequate, but vitamin C intake is insufficient." It recommended adding "100g orange (53mg vitamin C)" or "200g spinach (28mg vitamin C)" as a supplement. The assessment report was pushed to the app and a weekly nutrition intake trend chart was generated, showing that vitamin C intake was below the recommended value for three consecutive days.

[0056] Example 2:

[0057] If there are two children in the family (aged 3 and 8), their nutritional data needs to be tracked separately.

[0058] Account switching: Parents can switch to a 3-year-old child's account via fingerprint recognition;

[0059] Data synchronization: The system automatically links to the smart bracelet data to obtain the daily activity level (low intensity) of a 3-year-old child and dynamically adjusts the energy requirement calculation.

[0060] Daily energy requirement = (Basal metabolic rate 650kcal × 1.2) + growth compensation 50kcal = 830kcal;

[0061] Nutritional intervention: The system detected that the calcium intake was only 60% of the recommended value, and announced through the voice interaction unit: "It is recommended to add 100ml of milk or 30g of cheese to dinner."

[0062] Data storage: All intake data for the day is encrypted using blockchain and stored to generate an immutable record. Pediatricians can view historical data through an authorization code.

[0063] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A smart food identification, weighing, and children's nutrition assessment system, characterized in that, include: The weighing module is used to accurately weigh the ingredients. The image recognition module integrates a multispectral camera and an edge computing unit. It uses a convolutional neural network model to extract multi-level features from food images and identify the types and subcategories of food. The database module has a built-in latest version of the Chinese Food Composition Table, which stores the nutritional composition data of various ingredients per 100 grams. The calculation module, based on the weight data output by the weighing module and the food type identified by the image recognition module, extracts the corresponding nutritional data from the database module and calculates the actual intake according to the formula: Actual intake = weighed weight / 100 × nutrient value in database; The assessment module compares the total nutrient data output by the calculation module with the preset recommended intake for children's age groups, and generates a nutrition assessment report and dietary recommendations.

2. The intelligent food identification, weighing, and children's nutritional assessment system according to claim 1, characterized in that, The image recognition module uses a convolutional neural network model to support the classification and recognition of multiple categories of food ingredients.

3. The intelligent food identification, weighing, and children's nutritional assessment system according to claim 1, characterized in that, The weighing module and the image recognition module synchronize data via Bluetooth 5.0 protocol. The camera is automatically triggered to capture images during weighing, and the weight and recognition results are superimposed on the touch screen. Users can manually adjust the food classification or weight data.

4. The intelligent food identification, weighing, and children's nutrition assessment system according to claim 1, characterized in that, The database module incorporates a food equivalent substitution algorithm. When a certain food item is in short supply, it recommends alternative foods based on nutritional similarity and dynamically adjusts the intake calculation. The formula is as follows: The weight of the substitute ingredient = the target nutrient value of the original ingredient / the nutrient value per 100 grams of the substitute ingredient × 100.

5. The intelligent food identification, weighing, and children's nutritional assessment system according to claim 1, characterized in that, The assessment module integrates a children's health data interface, supporting manual input or synchronization of height, weight, and activity data through smart wearable devices. It dynamically adjusts the recommended intake based on the Harris-Benedict formula: Daily energy requirement = Basal metabolic rate × Activity coefficient + Growth compensation value, where the growth compensation value is set according to age and growth curve.

6. The intelligent food identification, weighing, and children's nutritional assessment system according to claim 1, characterized in that, The system supports a multi-user management mode, distinguishes user accounts through fingerprint or facial recognition, independently stores each child's historical nutrition data, and generates personalized trend analysis reports, including nutrient intake line graphs, target achievement rate radar charts, and percentiles compared to children of the same age.

7. The intelligent food identification, weighing, and children's nutritional assessment system according to claim 1, characterized in that, The computing module incorporates blockchain technology to encrypt daily intake data and store it in a distributed ledger, ensuring that the data is immutable and supporting authorized access to historical records by medical institutions.

8. The intelligent food identification, weighing, and children's nutrition assessment system according to claim 1, characterized in that, The system has a built-in voice interaction unit that supports voice command control of weighing, querying of nutritional components and broadcasting of assessment results. It also uses natural language processing technology to analyze user questions, such as "Is today's protein intake up to standard?" 9. The intelligent food identification, weighing, and children's nutritional assessment system according to claim 1, characterized in that, The assessment module connects to the API of a third-party nutritional supplement e-commerce platform. When it detects that the intake of a specific nutrient is insufficient for three consecutive days, the system will provide corresponding dietary improvement suggestions or recipes, and can also automatically generate purchase links to recommend nutritional supplements.

10. The intelligent food identification, weighing, and children's nutritional assessment system according to claim 1, characterized in that, The system is equipped with an ultraviolet disinfection unit integrated under the weighing tray. It automatically starts a 30-second sterilization cycle after each use to ensure the hygiene and safety of the food contact surfaces.

Images